Adapter is a function for conversion network infer output to metric specific format. You can use 2 ways to set adapter for topology:
type: for setting adapter name. This approach gives opportunity to set additional parameters for adapter if it is required.AccuracyChecker supports following set of adapters:
classification - converting output of classification model to ClassificationPrediction representation.segmentation - converting output of semantic segmentation model to SeegmentationPrediction representation.make_argmax - allows to apply argmax operation to output values.segmentation_one_class - converting output of semantic segmentation to SeegmentationPrediction representation. It is suitable for situation when model's output is probability of belong each pixel to foreground class.threshold - minimum probability threshold for valid class belonging.tiny_yolo_v1 - converting output of Tiny YOLO v1 model to DetectionPrediction representation.reid - converting output of reidentification model to ReIdentificationPrediction representation.grn_workaround - enabling processing output with adding Global Region Normalization layer.yolo_v2 - converting output of YOLO v2 family models to DetectionPrediction representation.classes - number of detection classes (default 20).anchors - anchor values provided as comma-separated list or one of precomputed:yolo_v2 - [1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071],tiny_yolo_v2 - [1.08, 1.19, 3.42, 4.41, 6.63, 11.38, 9.42, 5.11, 16.62, 10.52]coords - number of bbox coordinates (default 4).num - num parameter from DarkNet configuration file (default 5).cells - number of cells across width and height (default 13).raw_output - enabling additional preprocessing for raw YOLO output format (default False).output_format - setting output layer format:BHW - boxes first (default, also default for generated IRs).HWB - boxes last. Applicable only if network output not 3D (4D with batch) tensor.yolo_v3 - converting output of YOLO v3 family models to DetectionPrediction representation.classes - number of detection classes (default 80).anchors - anchor values provided as comma-separited list or precomputed:yolo_v3 - [10.0, 13.0, 16.0, 30.0, 33.0, 23.0, 30.0, 61.0, 62.0, 45.0, 59.0, 119.0, 116.0, 90.0, 156.0, 198.0, 373.0, 326.0]tiny_yolo_v3 - [10.0, 14.0, 23.0, 27.0, 37.0, 58.0, 81.0, 82.0, 135.0, 169.0, 344.0, 319.0]coords - number of bbox coordinates (default 4).num - num parameter from DarkNet configuration file (default 3).anchor_mask - mask for used anchors for each output layer (Optional, if not provided default way for selecting anchors will be used.)threshold - minimal objectness score value for valid detections (default 0.001).input_width and input_height - network input width and height correspondingly (default 416).outputs - the list of output layers names.raw_output - enabling additional preprocessing for raw YOLO output format (default False).output_format - setting output layer format - boxes first (BHW)(default, also default for generated IRs), boxes last (HWB). Applicable only if network output not 3D (4D with batch) tensor.cells - sets grid size for each layer, according outputs filed. Works only with do_reshape=True or when output tensor dimensions not equal 3.do_reshape - forces reshape output tensor to [B,Cy,Cx] or [Cy,Cx,B] format, depending on output_format value ([B,Cy,Cx] by default). You may need to specify cells value.lpr - converting output of license plate recognition model to CharacterRecognitionPrediction representation.ssd - converting output of SSD model to DetectionPrediction representation.ssd_mxnet - converting output of SSD-based models from MXNet framework to DetectionPrediction representation.pytorch_ssd_decoder - converts output of SSD model from PyTorch without embedded decoder.scores_out - name of output layer with bounding boxes scores.boxes_out - name of output layer with bounding boxes coordinates.confidence_threshold - lower bound for valid boxes scores (optional, default 0.05).nms_threshold - overlap threshold for NMS (optional, default 0.5).keep_top_k - maximal number of boxes which should be kept (optional, default 200).feat_size - features size in format [feature_width, feature_height], ...do_softmax - boolean flag which says should be softmax applied to detection scores or not. (Optional, default True)ssd_onnx - converting output of SSD-based model from PyTorch with NonMaxSuppression layer.labels_out - name of output layer with labels or regular expression for it searching.scores_out- name of output layer with scores or regular expression for it searching.bboxes_out - name of output layer with bboxes or regular expression for it searching.tf_object_detection - converting output of detection models from TensorFlow object detection API to DetectionPrediction.classes_out - name of output layer with predicted classes.boxes_out - name of output layer with predicted boxes coordinates in format [y0, x0, y1, x1].scores_out - name of output layer with detection scores.num_detections_out - name of output layer which contains the number of valid detections.retinanet - converting output of RetinaNet-based model.loc_out - name of output layer with bounding box deltas.class_out - name of output layer with classification probabilities.rfcn_class_agnostic - convert output of Caffe RFCN model with agnostic bounding box regression approach.cls_out - the name of output layer with detected probabilities for each class. The layer shape is [num_boxes, num_classes], where num_boxes is number of predicted boxes, num_classes - number of classes in the dataset including background.bbox_out - the name of output layer with detected boxes deltas. The layer shape is [num_boxes, 8] where num_boxes is number of predicted boxes, 8 (4 for background + 4 for foreground) bouding boxes coordinates.roid_out - the name of output layer with regions of interest.face_person_detection - converting face person detection model output with 2 detection outputs to ContainerPredition, where value of parameters face_outand person_out are used for identification DetectionPrediction in container.face_out - face detection output layer name.person_out - person detection output layer name.person_attributes - converting person attributes recognition model output to MultiLabelRecognitionPrediction.attributes_recognition_out - output layer name with attributes scores. (optional, used if your model has more than one outputs).vehicle_attributes - converting vehicle attributes recognition model output to ContainerPrediction where value of parameters color_outand type_out are used for identification ClassificationPrediction in container.color_out - vehicle color attribute output layer name.type_out- vehicle type attribute output layer name.head_pose - converting head pose estimation model output to ContainerPrediction where names of parameters angle_pitch, angle_yaw and angle_roll are used for identification RegressionPrediction in container.angle_pitch - output layer name for pitch angle.angle_yaw- output layer name for yaw angle.angle_roll - output layer name for roll angle.age_gender - converting age gender recognition model output to ContainerPrediction with ClassificationPrediction named gender for gender recognition, ClassificationPrediction named age_classification and RegressionPrediction named age_error for age recognition.age_out - output layer name for age recognition.gender_out - output layer name for gender recognition.action_detection - converting output of model for person detection and action recognition tasks to ContainerPrediction with DetectionPrdiction for class agnostic metric calculation and ActionDetectionPrediction for action recognition. The representations in container have names class_agnostic_prediction and action_prediction respectively.priorbox_out - name of layer containing prior boxes in SSD format.loc_out - name of layer containing box coordinates in SSD format.main_conf_out - name of layer containing detection confidences.add_conf_out_prefix - prefix for generation name of layers containing action confidences if topology has several following layers or layer name.add_conf_out_count - number of layers with action confidences (optional, you can not provide this argument if action confidences contained in one layer).num_action_classes - number classes for action recognition.detection_threshold - minimal detection confidences level for valid detections.actions_scores_threshold - minimal actions confidences level for valid detections.action_scale - scale for correct action score calculation.image_processing - converting output of network for single image processing to ImageProcessingPrediction.reverse_channels - allow switching output image channels e.g. RGB to BGR (Optional. Default value is False).mean - value or list channel-wise values which should be added to result for getting values in range [0, 255] (Optional, default 0)std - value or list channel-wise values on which result should be multiplied for getting values in range [0, 255] (Optional, default 255) Important Usually mean and std are the same which used in preprocessing, here they are used for reverting these preprocessing operations. The order of actions:stdmeantarget_out - target model output layer name in case when model has several outputs.super_resolution - converting output of single image super resolution network to SuperResolutionPrediction.reverse_channels - allow switching output image channels e.g. RGB to BGR (Optional. Default value is False).mean - value or list channel-wise values which should be added to result for getting values in range [0, 255] (Optional, default 0)std - value or list channel-wise values on which result should be multiplied for getting values in range [0, 255] (Optional, default 255)cast_to_uint8 - perform casting output image pixels to [0, 255] range. Important Usually mean and std are the same which used in preprocessing, here they are used for reverting these preprocessing operations. The order of actions:stdmeantarget_out - super resolution model output layer name in case when model has several outputs.multi_target_super_resolution - converting output super resolution network with multiple outputs to ContainerPrediction with SuperResolutionPrediction for each output.reverse_channels - allow switching output image channels e.g. RGB to BGR (Optional. Default value is False).mean - value or list channel-wise values which should be added to result for getting values in range [0, 255] (Optional, default 0)std - value or list channel-wise values on which result should be multiplied for getting values in range [0, 255] (Optional, default 255)cast_to_uint8 - perform casting output image pixels to [0, 255] range. Important Usually mean and std are the same which used in preprocessing, here they are used for reverting these preprocessing operations. The order of actions:stdmeantarget_mapping - dictionary where keys are meaningful name for solved task which will be used as keys inside ConverterPrediction, values - output layer names.super_resolution_yuv - converts output of super resolution model, which return output in YUV format, to SuperResolutionPrediction. Each output layer contains only 1 channel.y_output - Y channel output layer.u_output - U channel output layer.v_output - V channel output layer.target_color - taret color space for super resolution image - bgr and rgb are supported. (Optional, default bgr).landmarks_regression - converting output of model for landmarks regression to FacialLandmarksPrediction.pixel_link_text_detection - converting output of PixelLink like model for text detection to TextDetectionPrediction.pixel_class_out - name of layer containing information related to text/no-text classification for each pixel.pixel_link_out - name of layer containing information related to linkage between pixels and their neighbors.pixel_class_confidence_threshold - confidence threshold for valid segmentation mask (Optional, default 0.8).pixel_link_confidence_threshold - confidence threshold for valid pixel links (Optional, default 0.8).min_area - minimal area for valid text prediction (Optional, default 0).min_height - minimal height for valid text prediction (Optional, default 0).ctpn_text_detection - converting output of CTPN like model for text detection to TextDetectionPrediction.cls_prob_out - name of output layer with class probabilities.bbox_pred_out - name of output layer with predicted boxes.min_size - minimal valid detected text proposals size (Optional, default 8).min_ratio - minimal width / height ratio for valid text line (Optional, default 0.5).line_min_score - minimal confidence for text line (Optional, default 0.9).text_proposals_width - minimal width for text proposal (Optional, default 16).min_num_proposals - minimal number for text proposals (Optional, default 2).pre_nms_top_n - saved top n proposals before NMS applying (Optional, default 12000).post_nms_top_n - saved top n proposals after NMS applying (Optional, default 1000).nms_threshold - overlap threshold for NMS (Optional, default 0.7).east_text_detection - converting output of EAST like model for text detection to TextDetectionPrediction.score_map_out - the name of output layer which contains score map.geometry_map_out - the name of output layer which contains geometry map.score_map_threshold - threshold for score map (Optional, default 0.8).nms_threshold - threshold for text boxes NMS (Optional, default 0.2).box_threshold - minimal confidence threshold for text boxes (Optional, default 0.1).human_pose_estimation - converting output of model for human pose estimation to PoseEstimationPrediction.part_affinity_fields_out - name of output layer with keypoints pairwise relations (part affinity fields).keypoints_heatmap_out - name of output layer with keypoints heatmaps. The output layers can be omitted if model has only one output layer - concatenation of this 2.beam_search_decoder - realization CTC Beam Search decoder for symbol sequence recognition, converting model output to CharacterRecognitionPrediction.beam_size - size of the beam to use during decoding (default 10).blank_label - index of the CTC blank label.softmaxed_probabilities - indicator that model uses softmax for output layer (default False).ctc_greedy_search_decoder - realization CTC Greedy Search decoder for symbol sequence recognition, converting model output to CharacterRecognitionPrediction.blank_label - index of the CTC blank label (default 0).gaze_estimation - converting output of gaze estimation model to GazeVectorPrediction.hit_ratio_adapter - converting output NCF model to HitRatioPrediction.brain_tumor_segmentation - converting output of brain tumor segmentation model to BrainTumorSegmentationPrediction.make_argmax - allows to apply argmax operation to output values. (default - False)label_order - sets mapping from output classes to dataset classes. For example: label_order: [3,1,2] means that class with id 3 from model's output matches with class with id 1 from dataset, class with id 1 from model's output matches with class with id 2 from dataset, class with id 2 from model's output matches with class with id 3 from dataset.nmt - converting output of neural machine translation model to MachineTranslationPrediction.vocabulary_file - file which contains vocabulary for encoding model predicted indexes to words (e. g. vocab.bpe.32000.de). Path can be prefixed with --models arguments.eos_index - index end of string symbol in vocabulary (Optional, used in cases when launcher does not support dynamic output shape for cut off empty prediction).bert_question_answering - converting output of BERT model trained to solve question answering task to QuestionAnsweringPrediction.bert_classification - converting output of BERT model trained for classification task to ClassificationPrediction.num_classes - number of predicted classes.classification_out - name of output layer with classification probabilities. (Optional, if not provided default first output blob will be used).human_pose_estimation_3d - converting output of model for 3D human pose estimation to PoseEstimation3dPrediction.features_3d_out - name of output layer with 3D coordinates maps.keypoints_heatmap_out - name of output layer with keypoints heatmaps.part_affinity_fields_out - name of output layer with keypoints pairwise relations (part affinity fields).ctdet - converting output of CenterNet object detection model to DetectionPrediction.center_heatmap_out - name of output layer with center points heatmaps.width_height_out - name of the output layer with object sizes.regression_out - name of the regression output with the offset prediction.mask_rcnn - converting raw outputs of Mask-RCNN to combination of DetectionPrediction and CoCocInstanceSegmentationPrediction.classes_out - name of output layer with information about classes (optional, if your model has detection_output layer as output).scores_out - name of output layer with bbox scores (optional, if your model has detection_output layer as output).boxes_out - name of output layer with bboxes (optional, if your model has detection_output layer as output).raw_masks_out - name of output layer with raw instances masks.num_detections_out - name of output layer with number valid detections (used in MaskRCNN models trained with TF Object Detection API).detection_out - SSD-like detection output layer name (optional, if your model has scores_out, boxes_out and classes_out).mask_rcnn_with_text - converting raw outputs of Mask-RCNN with additional Text Recognition head to TextDetectionPrediction.classes_out - name of output layer with information about classes.scores_out - name of output layer with bbox scores.boxes_out - name of output layer with bboxes.raw_masks_out - name of output layer with raw instances masks.texts_out - name of output layer with texts.confidence_threshold - confidence threshold that is used to filter out detected instances.class_agnostic_detection - converting 'boxes' [n, 5] output of detection model to DetectionPrediction representation.output_blob - name of output layer with bboxes.scale - scalar value to normalize bbox coordinates.mono_depth - converting output of monocular depth estimation model to DepthEstimationPrediction.inpainting - converting output of Image Inpainting model to ImageInpaintingPrediction representation.style_transfer - converting output of Style Transfer model to StyleTransferPrediction representation.retinaface - converting output of RetinaFace model to DetectionPrediction or representation container with DetectionPrediction, AttributeDetectionPrediction, FacialLandmarksPrediction (depends on provided set of outputs)scores_outputs - the list of names for output layers with face detection score in order belonging to 32-, 16-, 8-strides.bboxes_outputs - the list of names for output layers with face detection boxes in order belonging to 32-, 16-, 8-strides.landmarks_outputs - the list of names for output layers with predicted facial landmarks in order belonging to 32-, 16-, 8-strides (optional, if not provided, only DetectionPrediction will be generated).type_scores_outputs - the list of names for output layers with attributes detection score in order belonging to 32-, 16-, 8-strides (optional, if not provided, only DetectionPrediction will be generated).faceboxes - converting output of FaceBoxes model to DetectionPrediction representation.scores_out - name of output layer with bounding boxes scores.boxes_out - name of output layer with bounding boxes coordinates.prnet - converting output of PRNet model for 3D landmarks regression task to FacialLandmarks3DPredictionlandmarks_ids_file - the file with indeces for landmarks extraction from position heatmap. (Optional, default values defined here)person_vehicle_detection - converts output of person vehicle detection model to DetectionPrediction representation. Adapter merges scores, groups predictions into people and vehicles, and assignes labels accordingly.iou_threshold - IOU threshold value for NMS operation.face_detection - converts output of face detection model to DetectionPrediction representation. Operation is performed by mapping model output to the defined anchors, window scales, window translates, and window lengths to generate a list of face candidates.score_threshold - Score threshold value used to discern whether a face is valid.layer_names - Target output layer base names.anchor_sizes - Anchor sizes for each base output layer.window_scales - Window scales for each base output layer.window_lengths - Window lengths for each base output layer.face_detection_refinement - converts output of face detection refinement model to DetectionPrediction representation. Adapter refines candidates generated in previous stage model.threshold - Score threshold to determine as valid face candidate.attribute_classification - converts output of attributes classifcation model to ContainerPrediction which contains multiple ClassificationPrediction for attributes with their scores.output_layer_map - dictionary where keys are output layer names of attribute classification model and values are the names of attributes.